Computer Sciences Commons^™

Content-Based Visual Landmark Search Via Multimodal Hypergraph Learning, Lei Zhu, Jialie Shen, Hai Jin, Ran Zheng, Liang Xie

Research Collection School Of Computing and Information Systems

While content-based landmark image search has recently received a lot of attention and became a very active domain, it still remains a challenging problem. Among the various reasons, high diverse visual content is the most significant one. It is common that for the same landmark, images with a wide range of visual appearances can be found from different sources and different landmarks may share very similar sets of images. As a consequence, it is very hard to accurately estimate the similarities between the landmarks purely based on single type of visual feature. Moreover, the relationships between landmark images can be …

Differentially Private Subspace Clustering, Yining Wang, Yu-Xiang Wang, Aarti Singh

Research Collection School Of Computing and Information Systems

Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple “clusters” so that data points in a single cluster lie approximately on a low-dimensional linear subspace. It is originally motivated by 3D motion segmentation in computer vision, but has recently been generically applied to a wide range of statistical machine learning problems, which often involves sensitive datasets about human subjects. This raises a dire concern for data privacy. In this work, we build on the framework of differential privacy and present two provably private subspace clustering algorithms. We demonstrate via both theory and experiments that …

On The Unreliability Of Bug Severity Data, Yuan Tian, Nasir Ali, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Severity levels, e.g., critical and minor, of bugs are often used to prioritize development efforts. Prior research efforts have proposed approaches to automatically assign the severity label to a bug report. All prior efforts verify the accuracy of their approaches using human-assigned bug reports data that is stored in software repositories. However, all prior efforts assume that such human-assigned data is reliable. Hence a perfect automated approach should be able to assign the same severity label as in the repository – achieving a 100% accuracy. Looking at duplicate bug reports (i.e., reports referring to the same problem) from three open-source …

Capstone Projects Mining System For Insights And Recommendations, Melvrivk Aik Chun Goh, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

In this paper, we present a classification based system to discover knowledge and trends in higher education students’ projects. Essentially, the educational capstone projects provide an opportunity for students to apply what they have learned and prepare themselves for industry needs. Therefore mining such projects gives insights of students’ experiences as well as industry project requirements and trends. In particular, we mine capstone projects executed by Information Systems students to discover patterns and insights related to people, organization, domain, industry needs and time. We build a capstone projects mining system (CPMS) based on classification models that leverage text mining, natural …

A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma

Research Collection School Of Computing and Information Systems

We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in …

Building Crowd Movement Model Using Sample-Based Mobility Survey, Larry J. J. Lin, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Crowd simulation is a well-studied topic, yet it usually focuses on visualization. In this paper, we study a special class of crowd simulation, where individual agents have diverse backgrounds, ad hoc objectives, and non-repeating visits. Such crowd simulation is particularly useful when modeling human agents movement in leisure settings such as visiting museums or theme parks. In these settings, we are interested in accurately estimating aggregate crowd-related movement statistics. As comprehensive monitoring is usually not feasible for a large crowd, we propose to conduct mobility surveys on only a small group of sampled individuals. We demonstrate via simulation that we …

Mopeye: Monitoring Per-App Network Performance With Zero Measurement Traffic, Daoyuan Wu, Weichao Li, Rocky K. C. Chang, Debin Gao

Research Collection School Of Computing and Information Systems

Mobile network performance measurement is important for understanding mobile user experience, problem diagnosis, and service comparison. A number of crowdsourcing measurement apps (e.g., MobiPerf and Netalyzr) have been embarked for the last few years. Unlike existing apps that use active measurement methods, we employ a novel passive-active approach to continuously monitor per-app network performance on unrooted smartphones without injecting additional network traffic. By leveraging the VpnService API on Android, MopEye, our measurement app, intercepts all network traffic and then relays them to their destinations using socket APIs. Therefore, not only MopEye can measure the round-trip time accurately, it can do …

Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved …

Robust Execution Strategies For Project Scheduling With Unreliable Resources And Stochastic Durations, Na Fu, Hoong Chuin Lau, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

The resource-constrained project scheduling problem with minimum and maximum time lags (RCPSP/max) is a general model for resource scheduling in many real-world problems (such as manufacturing and construction engineering). We consider RCPSP/max problems where the durations of activities are stochastic and resources can have unforeseen breakdowns. Given a level of allowable risk, (Formula presented.), our mechanisms aim to compute the minimum robust makespan execution strategy. Robust makespan for an execution strategy is any makespan value that has a risk less than (Formula presented.). The risk for a makespan value, (Formula presented.) given an execution strategy, is the probability that a …

Identifying Student Behavior For Improving Online Course Performance With Machine Learning, Makoto Mori

Theses and Dissertations

In this study we investigate the correlation between student behavior and performance in online courses. Based on the web logs and syllabus of a course, we extract features that characterize student behavior. Using machine learning algorithms, we build models to predict performance at end of the period. Furthermore, we identify important behavior and behavior combinations in the models. The result of prediction in three tasks reach 87% accurate on average without using any score related features in the first half of the semester.

Automated Multi-Modal Search And Rescue Using Boosted Histogram Of Oriented Gradients, Matthew A. Lienemann

Master's Theses

Unmanned Aerial Vehicles (UAVs) provides a platform for many automated tasks and with an ever increasing advances in computing, these tasks can be more complex. The use of UAVs is expanded in this thesis with the goal of Search and Rescue (SAR), where a UAV can assist fast responders to search for a lost person and relay possible search areas back to SAR teams. To identify a person from an aerial perspective, low-level Histogram of Oriented Gradients (HOG) feature descriptors are used over a segmented region, provided from thermal data, to increase classification speed. This thesis also introduces a dataset …

Motion Analysis Using Wearable Wireless Sensors To Support Treatment Of Diseases In The Elderly, Calvin Hoi Kok Cheung, Timothy Kin Sang Lee

Practical Social and Industrial Research Symposium

No abstract provided.

The Effectiveness Of A Smart School Bag System For Reminding Students Of Forgotten Items And Reducing The Weight Of Their Bags, Sau Ming Lau, Yammy Wai Yan Wong, Fiona Wing Yin Luk, Stella Sin Tung Kwok

Practical Social and Industrial Research Symposium

No abstract provided.

The Mathematics And Applications Behind Image Warping And Morphing, Tanvir Prince, Maria Malik, Ildefonso Salva, Ariel Mazor, Sakhr Aldaylam

Publications and Research

This research is conducted in the summer of 2015 and is possible by the support of various agency, in particular, by the grant of Prof. Angulo Nieves and the New York City Research Initiative.

The purpose of this research is to reveal the mathematics and applications of the computer animation techniques of warping and morphing. A warp is a twist or distortion in the form of an object in an image while a morph is the smooth and gradual transformation of an object in one image into the object in another image. Linear algebra makes these computer animation techniques possible; …

Maximum Priority Matchings, Jonathan Turner

All Computer Science and Engineering Research

Let G=(V,E) be an undirected graph with n vertices and m edges, in which each vertex u is assigned an integer priority in [1,n], with 1 being the ``highest'' priority. Let M be a matching of G. We define the priority score of M to be an n-ary integer in which the i-th most-significant digit is the number of vertices with priority i that are incident to an edge in M. We describe a variation of the augmenting path method (Edmonds' algorithm) that finds a matching with maximum priority score in O(mn) time.

Dogcat -- An Educational Word Game Via Computer, Muneam Jabbar Hamzah

Theses and Dissertations

DOGCAT is an educational game related to an Artificial Intelligence problem, the goal of implementing it is to help kids and young people to improve their abilities in vocabulary learning in an amusing way. In the version of DOGCAT I am proposing, several enhancements to the basic game are introduced. First, the user can choose whether he/she wants to play with three or four-letter words. (The four letter version is genuinely difficult to play, and so a computerized version would be of interest to adults as well as youngsters.) Also, the user has a choice to change the cost between …

Motionauth: Motion-Based Authentication For Wrist Worn Smart Devices, Junshuang Yang

Theses and Dissertations

Wrist worn smart devices are increasingly popular. As those devices collect sensitive personal information and often work as a companion component of smart phones, thus being treated trustworthy, appropriate user authentication mechanisms are necessary to prevent illegitimate access to those devices. However, the small form and function-based usage of wearable devices also pose a big challenge to authentication, which should be user friendly and unobtrusive. We propose MotionAuth, a behavioral biometric based authentication method, which collects user’s behavioral biometrics through a wrist worn smart device for building a user’s profile, and applies the profile in user validation. We implement MotionAuth …

Predictive Analytics Of Organizational Decisions And The Role Of Rationality, Arash Barfar

USF Tampa Graduate Theses and Dissertations

How can we predict key decisions made by organizations in the presence of big data and on-demand information? In this dissertation we exploit a large repository of B2B real-time transactional data with service quality indicators and present evidence that organizational decision analytics apply both rational and boundedly-rational (i.e. behavioral) economic models. The dissertation’s findings demonstrate that both utility and heuristic models, respectively, play significant roles in predicting organizational decisions on churn, a key decision in this context. In the presence of a large data set the assumed rationality of organizations appears to provide accurate predictions in uncontrolled experiences and selected …

Direct Solutions To Perceptual Organization Problems, Ravi Kumar Panchumarthy

USF Tampa Graduate Theses and Dissertations

Quadratic optimization problems arise in various real world application domains including engineering design, microeconomics, genetic algorithms, integrated circuit chip design, probabilistic graphical models and computer vision. In particular, there are many problems in computer vision that require binary quadratic optimization such as motion segmentation, correspondences, figure-ground segmentation, clustering, grouping, subgraph matching, and digital matting. The objective of an optimization algorithm can be related to the state of a physical system, where the goal is to bring the initial arbitrary state of the system to a state with minimum possible energy. By recognizing that the Hamiltonian of nanomagnets can be expressed …

Functionalized Nanodiamond Reinforced Biopolymers, Gongyao Zhou, Peter I. Lelkes, Yury Gogotsi, Vadym Mochalin

Chemistry Faculty Research & Creative Works

The present invention includes a composition for implantation in a patient, comprising surface-functionalized nanodiamonds and at least one biodegradable biocompatible polymer. The present invention also includes a surgical fixation device for use in a patient.

Content Abuse And Privacy Concerns In Online Social Networks, Md Imrul Kayes

USF Tampa Graduate Theses and Dissertations

Online Social Networks (OSNs) have seen an exponential growth over the last decade, with Facebook having more than 1.49 billion monthly active users and Twitter having 135,000 new users signing up every day as of 2015. Users are sharing 70 million photos per day on the Instagram photo-sharing network. Yahoo Answers question-answering community has more than 1 billion posted answers. The meteoric rise in popularity has made OSNs important social platforms for computer-mediated communications and embedded themselves into society’s daily life, with direct consequences to the offline world and activities. OSNs are built on a foundation of trust, where users …

Computational Methods For Biomarker Identification In Complex Disease, Amin Ahmadi Adl

USF Tampa Graduate Theses and Dissertations

In a modern systematic view of biology, cell functions arise from the interaction between molecular components. One of the challenging problems in systems biology with high-throughput measurements is discovering the important components involved in the development and progression of complex diseases, which may serve as biomarkers for accurate predictive modeling and as targets for therapeutic purposes. Due to the non-linearity and heterogeneity of these complex diseases, traditional biomarker identification approaches have had limited success at finding clinically useful biomarkers. In this dissertation we propose novel methods for biomarker identification that explicitly take into account the non-linearity and heterogeneity of complex …

Information Technology Services (Its) Program Database, Lora Ersland

Masters Theses & Doctoral Dissertations

The South Dakota Board of Regents (SDBOR) system, comprised of the six public universities, has undertaken a project to migrate the Colleague Student Information System from a proprietary Unidata database to an Oracle database. The conversion to the Oracle database will allow the ITS Administrative Computing staff more options to create programs that access the Colleague Student Information System.

During the conversion of the existing Unidata programs, it was discovered that the migration was causing us to lose our ability to keep track of programs that were developed to access the Colleague system. In addition, while we are gaining the …

The Role Of Cryptography In Security For Electronic Commerce, Ann Murphy, David Murphy

The ITB Journal

Many businesses and consumers are wary of conducting business over the Internet due to a perceived lack of security. Electronic business is subject to a variety of threats such as unauthorised access, misappropriation, alteration and destruction of both data and systems. This paper explores the major security concerns of businesses and users and describes the cryptographic techniques used to reduce such risks.

Mlaas: Machine Learning As A Service, Mauro Ribeiro, Katarina Grolinger, Miriam Am Capretz

Electrical and Computer Engineering Publications

The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was …

An Adaptive Markov Strategy For Effective Network Intrusion Detection, Jianye Hao, Yinxing Xue, Mahinthan Chandramohan, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

Network monitoring is an important way to ensure the security of hosts from being attacked by malicious attackers. One challenging problem for network operators is how to distribute the limited monitoring resources (e.g., intrusion detectors) among the network to detect attacks effectively, especially when the attacking strategies can be changing dynamically and unpredictable. To this end, we adopt Markov game to model the interactions between the network operator and the attacker and propose an adaptive Markov strategy (AMS) to determine how the detectors should be placed on the network against possible attacks to minimize the network’s accumulated cost over time. …

Morphological Parsing Of Albanian Language: A Different Approach To Albanian Verbs, Iir Çollaku, Eşref Adali

UBT International Conference

The very first step when processing a natural language is creating a morphological parser. Verbs in Albanian language are the most complex area of inflection. Besides irregular verbs, the ways in which the regular verbs change their form while being inflected are hardly definable, and the number of exceptions is huge. In this paper, a different approach to Albanian verbs is made. Unlike traditional classification, based on the inflection themes they take, verbs are classified into different verb groups. This way, the inflection process looks clearer and more regular, as the affix remains the only changeable part of the inflected …

Microsimulation Models For Simulating Pathways Of Patients With Mental Diseases, Andreas Bauer, Felix Breitenecker, Christoph Urach

UBT International Conference

Predicting demand for care is necessary to provide sufficient capacities in hospitals and enable better planning to cope with challenges like changes in the structure of the population. Emphasis in this work is put on the analysis of the data and on the parametrization of a simulation model for the paths of patients with mental diseases through the health care system. Survival analysis and model selection methods are used for this purpose. Data on patients and their treatments is analyzed with methods of survival analysis. Different methods for modelling the survival and hazard function are presented and compared. Hereby, the …

Introduction To Stratospheric Communication Platforms (Scp), Dimov Stojce Ilcev

UBT International Conference

In this paper are introduced the modern airship techniques and technologies as cost effective solutions of Stratospheric Communication Platforms (SCP). The launch or putting in position the airship is not critical point such as launch of satellite and controlling support services in the creation of space-based communication technology and the most expensive phase of the total system cost. Therefore, with few cost effective remote controlled and solar powered airships can be covered some region or country including urban, suburban and rural areas, mobile, farms and other environments with low density of population. The airship SCP network offers better solutions than …

New Integral Transform In Caputo Type Fractional Difference Operator, Artion Kashuri, Akli Fundo, Rozana Liko

UBT International Conference

In this paper, we introduce Caputo type nabla (q,h)-fractional difference operators and investigate their basic properties and also to show the applicability of this interesting (q,h)- new integral transform method and its efficiency in solving linear fractional difference equations. Differential equations with fractional derivative provide a natural framework for the discussion of various kinds of real problems modeled by the aid of fractional derivative. Discrete analogues of some topics of continuous fractional calculus have been developed. Finally, we provide the general solutions in terms of discrete Mittag-Leffler functions.